Service Recommendation Middleware Based on Location Privacy Protection in VANET

Abstract

With the help of location-based services (LBS), it makes driving more convience for drivers. However, because the untrusted LBS server may leak the user's location information, the user's privacy is threatened. Moreover, the existing methods of location privacy protection do not take into account the impact of context on privacy protection demand. In addition, heterogeneous data sensed by vehicles also increases the complexity of application development. In order to solve the above problems, we propose a context-based location privacy protection middleware architecture, named PP-OSGi. The middleware simplifies application development by shielding the heterogeneity of vehicle sensed data and upper-layer applications. Furthermore, in order to protect the real location information of service request vehicles under different vehicle densities in a certain area, we propose a dynamically adjustable k-anonymous (DAK) algorithm and a location privacy protection (DLP) algorithm based on a dummy location, which are all encapsulated in PP-OSGi. The DAK and DLP algorithms dynamically determine the location privacy protection strength in different contexts based on the user's location privacy preference model, select an anonymous group of neighboring vehicles to construct a anonymous area, and obtain a dummy location of the service request vehicle. The experimental results show that, under the premise of protecting the location privacy of vehicles, the success rate of service requests is improved and the communication cost between vehicles is reduced.

SGO: Semantic Group Obfuscation for Location-Based Services in VANETS

“…An anonymous area is created in Ref. [ 40 ], taking anonymous neighboring vehicles to obtain dummy locations of requested vehicles in a different context, which strengthened the location privacy of vehicles. To mislead an attacker, a target vehicle selects a shadow of other vehicles to generate multiple virtual trajectories for communication with the LBS server [ 18 ].…”

An Efficient Differential Privacy-Based Method for Location Privacy Protection in Location-Based Services

“…A blockchain-enabled framework for peer-to-peer (P2P) energy trading was designed in [13], and an anonymous proof-of-location algorithm was proposed that allows clients to choose their trading partners without revealing their real locations. Zheng et al [14] employed a dynamically adjustable k-anonymity (DAK) algorithm and a dynamical location privacy protection (DLPP) algorithm based on virtual locations in which sequences are disturbed by adding and deleting moving points. However, the effectiveness of combining l-diversity and k-anonymity is limited by data distribution and background knowledge attacks.…”

An Efficient Location Privacy Protection Method for Location-Based Services based on Differential Privacy

“…A blockchain-enabled framework for P2P energy trading was designed in [13], and an Anonymous Proof of Location algorithm is proposed that allows clients to choose their trading partners without revealing their real location. Zheng et al [14] employed a dynamically adjustable k-anonymity (DAK) algorithm and a dynamical location privacy protection (DLPP) algorithm based on virtual locations, in which sequences were disturbed by adding and deleting some moving points. Additionally the notion of l-diversity and kanonymity are greatly limited by data distribution and the background knowledge attacks.…”